Ensemble Learning. Another slightly related to our approach in metric learning is the ensemble learning [1]}, [2]}, [3]}. HDC [1]} ensembles a set of models with different complexities in a cascaded manner for mining hard examples at multiple levels. A-BIER [2]} divides the last embedding layer of a deep network into an embedding ensemble and formulate training this ensemble as an online gradient boosting problem. [3]} follows a divide-and-conquer strategy by splitting and subsequently merging both the data and embedding space.
The critical difference in our IDEAL is that we divide embedding subspace based on corresponding augmented domains. The model is independently trained on non-overlapping augmented domains in multi-task learning fashion. Finally, to fully exploit the value of each domain, we adopt an ensemble strategy during testing.
